Software Architecture Design

# System Scalability Checklist

Use this checklist when designing for horizontal scalability and high availability.

## Load Estimation

- [ ] **Current load:** [N] requests/second, [N] concurrent users
- [ ] **Peak load:** [N] requests/second (expected during [event/time])
- [ ] **Growth projection:** [X]% yearly growth
- [ ] **Target capacity:** Support [N]x current load

## Scalability Dimensions

### Horizontal Scaling (Preferred)

- [ ] **Stateless services:** All application servers are stateless
- [ ] **Session storage:** Use Redis/Memcached for distributed sessions
- [ ] **File storage:** Use object storage (S3/GCS) instead of local filesystem
- [ ] **Auto-scaling:** Configure based on CPU/memory/RPS metrics
- [ ] **Load balancer:** Layer 7 (application-aware) load balancing

### Vertical Scaling (Limited)

- [ ] **Database instance:** Right-sized for current + 6 months growth
- [ ] **Cache instance:** Sized for working set + 20% headroom
- [ ] **Max capacity:** Identified vertical scaling limit

## Database Scalability

### Read Scaling

- [ ] **Read replicas:** [N] replicas for read-heavy workloads
- [ ] **Read/write splitting:** Route reads to replicas, writes to primary
- [ ] **Connection pooling:** PgBouncer/ProxySQL to limit connections
- [ ] **Query optimization:** Queries < [10ms] with proper indexing

### Write Scaling

- [ ] **Sharding strategy:** [Hash-based / Range-based / Geographic]
  - Shard key: [user_id / tenant_id / region]
  - Number of shards: [N] (plan for [10x] growth)
- [ ] **Write-ahead logging:** Asynchronous replication for replicas
- [ ] **Bulk operations:** Batch inserts/updates to reduce round trips

### Caching Strategy

- [ ] **Cache layers:**
  - L1: In-memory application cache ([Caffeine / Guava])
  - L2: Distributed cache ([Redis / Memcached])
  - L3: CDN for static assets ([CloudFront / Fastly])
- [ ] **Cache hit ratio:** Target > [90]%
- [ ] **TTL strategy:** Balance freshness vs load ([5min] for hot data, [1h] for warm)
- [ ] **Cache eviction:** LRU/LFU policy configured
- [ ] **Cache warming:** Pre-populate on deployment
- [ ] **Cache invalidation:** Event-driven invalidation for updates

## API Gateway

- [ ] **Rate limiting:**
  - Per-user: [N] req/min
  - Per-IP: [N] req/min
  - Burst allowance: [N] requests
- [ ] **Request throttling:** Queue requests during spikes
- [ ] **Response compression:** Gzip/Brotli enabled
- [ ] **API versioning:** Support [N] concurrent versions

## Asynchronous Processing

- [ ] **Message queue:** [Kafka / RabbitMQ / AWS SQS]
  - Throughput: [N] messages/second
  - Retention: [X] days
- [ ] **Worker pools:** [N] workers per queue
- [ ] **Backpressure:** Reject requests when queue length > [N]
- [ ] **Dead letter queue:** For failed message handling

## Content Delivery

- [ ] **CDN:** CloudFront/Fastly for static assets
- [ ] **Edge caching:** Cache-Control headers configured
- [ ] **Image optimization:** WebP format, lazy loading
- [ ] **Asset bundling:** Minified and bundled CSS/JS

## Data Storage Patterns

### Hot/Warm/Cold Data

- [ ] **Hot data:** Last [7] days in primary DB (fast access)
- [ ] **Warm data:** Last [30] days in read replicas
- [ ] **Cold data:** Older than [30] days archived to S3/Glacier
- [ ] **Archival strategy:** Automated data lifecycle policies

### Data Partitioning

- [ ] **Time-based partitioning:** Partition by month/year for time-series data
- [ ] **Hash partitioning:** Distribute by hash(user_id) for even distribution
- [ ] **List partitioning:** Partition by region/tenant for isolation

## Connection Management

- [ ] **Database connection pool:**
  - Min connections: [N]
  - Max connections: [N]
  - Connection timeout: [Xms]
- [ ] **HTTP keep-alive:** Reuse connections to upstream services
- [ ] **Circuit breaker:** Prevent cascade failures

## Observability for Scalability

### Key Metrics

- [ ] **Golden signals:**
  - Latency: p50, p95, p99 response times
  - Traffic: Requests per second
  - Errors: Error rate